MetaboLINK is a novel algorithm for unveiling cell-specific metabolic pathways in longitudinal datasets
IntroductionIn the rapidly advancing field of ‘omics research, there is an increasing demand for sophisticated bioinformatic tools to enable efficient and consistent data analysis. As biological datasets, particularly metabolomics, become larger and more complex, innovative strategies are essential...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2024.1520982/full |
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| author | Jared Lichtarge Gerarda Cappuccio Gerarda Cappuccio Soumya Pati Soumya Pati Alfred Kwabena Dei-Ampeh Alfred Kwabena Dei-Ampeh Senghong Sing Senghong Sing Senghong Sing LiHua Ma Zhandong Liu Zhandong Liu Mirjana Maletic-Savatic Mirjana Maletic-Savatic Mirjana Maletic-Savatic |
| author_facet | Jared Lichtarge Gerarda Cappuccio Gerarda Cappuccio Soumya Pati Soumya Pati Alfred Kwabena Dei-Ampeh Alfred Kwabena Dei-Ampeh Senghong Sing Senghong Sing Senghong Sing LiHua Ma Zhandong Liu Zhandong Liu Mirjana Maletic-Savatic Mirjana Maletic-Savatic Mirjana Maletic-Savatic |
| author_sort | Jared Lichtarge |
| collection | DOAJ |
| description | IntroductionIn the rapidly advancing field of ‘omics research, there is an increasing demand for sophisticated bioinformatic tools to enable efficient and consistent data analysis. As biological datasets, particularly metabolomics, become larger and more complex, innovative strategies are essential for deciphering the intricate molecular and cellular networks.MethodsWe introduce a pioneering analytical approach that combines Principal Component Analysis (PCA) with Graphical Lasso (GLASSO). This method is designed to reduce the dimensionality of large datasets while preserving significant variance. For the first time, we applied the PCA-GLASSO algorithm (i.e., MetaboLINK) to metabolomics data derived from Nuclear Magnetic Resonance (NMR) spectroscopy performed on neural cells at various developmental stages, from human embryonic stem cells to neurons.ResultsThe MetaboLINK analysis of longitudinal metabolomics data has revealed distinct pathways related to amino acids, lipids, and energy metabolism, uniquely associated with specific cell progenies. These findings suggest that different metabolic pathways play a critical role at different stages of cellular development, each contributing to diverse cellular functions.DiscussionOur study demonstrates the efficacy of the MetaboLINK approach in analyzing NMR-based longitudinal metabolomic datasets, highlighting key metabolic shifts during cellular transitions. We share the methodology and the code to advance general ‘omics research, providing a powerful tool for dissecting large datasets in neurobiology and other fields. |
| format | Article |
| id | doaj-art-85aacb0478a14fc3917f527123456b80 |
| institution | Kabale University |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neuroscience |
| spelling | doaj-art-85aacb0478a14fc3917f527123456b802025-01-13T06:11:06ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-01-011810.3389/fnins.2024.15209821520982MetaboLINK is a novel algorithm for unveiling cell-specific metabolic pathways in longitudinal datasetsJared Lichtarge0Gerarda Cappuccio1Gerarda Cappuccio2Soumya Pati3Soumya Pati4Alfred Kwabena Dei-Ampeh5Alfred Kwabena Dei-Ampeh6Senghong Sing7Senghong Sing8Senghong Sing9LiHua Ma10Zhandong Liu11Zhandong Liu12Mirjana Maletic-Savatic13Mirjana Maletic-Savatic14Mirjana Maletic-Savatic15Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, United StatesJan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, United StatesDepartment of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX, United StatesJan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, United StatesDepartment of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX, United StatesJan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, United StatesDepartment of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX, United StatesJan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, United StatesDepartment of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX, United StatesCollege of Natural Sciences and Mathematics, University of Houston, Houston, TX, United StatesShared Equipment Authority, Rice University, Houston, TX, United StatesJan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, United StatesDepartment of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX, United StatesJan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, United StatesDepartment of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX, United StatesDepartment of Neuroscience, Baylor College of Medicine, Houston, TX, United StatesIntroductionIn the rapidly advancing field of ‘omics research, there is an increasing demand for sophisticated bioinformatic tools to enable efficient and consistent data analysis. As biological datasets, particularly metabolomics, become larger and more complex, innovative strategies are essential for deciphering the intricate molecular and cellular networks.MethodsWe introduce a pioneering analytical approach that combines Principal Component Analysis (PCA) with Graphical Lasso (GLASSO). This method is designed to reduce the dimensionality of large datasets while preserving significant variance. For the first time, we applied the PCA-GLASSO algorithm (i.e., MetaboLINK) to metabolomics data derived from Nuclear Magnetic Resonance (NMR) spectroscopy performed on neural cells at various developmental stages, from human embryonic stem cells to neurons.ResultsThe MetaboLINK analysis of longitudinal metabolomics data has revealed distinct pathways related to amino acids, lipids, and energy metabolism, uniquely associated with specific cell progenies. These findings suggest that different metabolic pathways play a critical role at different stages of cellular development, each contributing to diverse cellular functions.DiscussionOur study demonstrates the efficacy of the MetaboLINK approach in analyzing NMR-based longitudinal metabolomic datasets, highlighting key metabolic shifts during cellular transitions. We share the methodology and the code to advance general ‘omics research, providing a powerful tool for dissecting large datasets in neurobiology and other fields.https://www.frontiersin.org/articles/10.3389/fnins.2024.1520982/fullmetabolomePCAGlassoMetaboLINKhESCembryonic bodies |
| spellingShingle | Jared Lichtarge Gerarda Cappuccio Gerarda Cappuccio Soumya Pati Soumya Pati Alfred Kwabena Dei-Ampeh Alfred Kwabena Dei-Ampeh Senghong Sing Senghong Sing Senghong Sing LiHua Ma Zhandong Liu Zhandong Liu Mirjana Maletic-Savatic Mirjana Maletic-Savatic Mirjana Maletic-Savatic MetaboLINK is a novel algorithm for unveiling cell-specific metabolic pathways in longitudinal datasets Frontiers in Neuroscience metabolome PCA Glasso MetaboLINK hESC embryonic bodies |
| title | MetaboLINK is a novel algorithm for unveiling cell-specific metabolic pathways in longitudinal datasets |
| title_full | MetaboLINK is a novel algorithm for unveiling cell-specific metabolic pathways in longitudinal datasets |
| title_fullStr | MetaboLINK is a novel algorithm for unveiling cell-specific metabolic pathways in longitudinal datasets |
| title_full_unstemmed | MetaboLINK is a novel algorithm for unveiling cell-specific metabolic pathways in longitudinal datasets |
| title_short | MetaboLINK is a novel algorithm for unveiling cell-specific metabolic pathways in longitudinal datasets |
| title_sort | metabolink is a novel algorithm for unveiling cell specific metabolic pathways in longitudinal datasets |
| topic | metabolome PCA Glasso MetaboLINK hESC embryonic bodies |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2024.1520982/full |
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